{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Python\\lib\\site-packages\\pandas\\core\\arrays\\masked.py:64: UserWarning: Pandas requires version '1.3.2' or newer of 'bottleneck' (version '1.2.1' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "my_seed = 2017\n",
    "np.random.seed(my_seed)\n",
    "import random \n",
    "random.seed(my_seed)\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(my_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>link_id</th>\n",
       "      <th>throughput</th>\n",
       "      <th>delay</th>\n",
       "      <th>jitter</th>\n",
       "      <th>loss</th>\n",
       "      <th>入端口</th>\n",
       "      <th>出端口</th>\n",
       "      <th>组合</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00:56:56.600000</td>\n",
       "      <td>4-3-2-5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.355648</td>\n",
       "      <td>1.355648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00:57:09.600000</td>\n",
       "      <td>4-3-2-5</td>\n",
       "      <td>3200</td>\n",
       "      <td>1.294017</td>\n",
       "      <td>0.061631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00:57:36.700000</td>\n",
       "      <td>4-3-2-5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.390100</td>\n",
       "      <td>1.390100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00:57:49.700000</td>\n",
       "      <td>4-3-2-5</td>\n",
       "      <td>4930</td>\n",
       "      <td>1.552820</td>\n",
       "      <td>0.162721</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         timestamp  link_id  throughput     delay    jitter  loss  入端口  出端口  \\\n",
       "0  00:56:56.600000  4-3-2-5           0  1.355648  1.355648   0.0    4    5   \n",
       "1  00:57:09.600000  4-3-2-5        3200  1.294017  0.061631   0.0    4    5   \n",
       "2  00:57:36.700000  4-3-2-5           0  1.390100  1.390100   0.0    4    5   \n",
       "3  00:57:49.700000  4-3-2-5        4930  1.552820  0.162721   0.0    4    5   \n",
       "\n",
       "   组合  \n",
       "0  45  \n",
       "1  45  \n",
       "2  45  \n",
       "3  45  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入数据测试\n",
    "df2='45.xlsx'\n",
    "data2=pd.read_excel(df2)\n",
    "data2.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   0 3200    0 ...  672  672  720]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建一个Series对象\n",
    "s = data2[\"throughput\"]\n",
    "# 将Series转换为ndarray\n",
    "arr = s.to_numpy()\n",
    "# 输出数组\n",
    "print(arr)\n",
    "#检查数据类型\n",
    "type(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams['font.family'] = 'SimHei'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#all_data = np.fromfile(\"ec_data\")\n",
    "all_data=arr\n",
    "type(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "stand_scaler = MinMaxScaler()\n",
    "all_data = stand_scaler.fit_transform(all_data.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sequence_len = 10 #原来的\n",
    "sequence_len = 60 #最适合\n",
    "X = []\n",
    "Y = []\n",
    "for i in range(len(all_data)-sequence_len):\n",
    "    X.append(all_data[i:i+sequence_len])\n",
    "    Y.append(all_data[i+sequence_len])\n",
    "X = np.array(X)\n",
    "Y = np.array(Y)\n",
    "#这段代码的作用是将一个给定时间序列数据 `all_data` 按照指定的时间步长 `sequence_len` 进行滑窗切分，切分出一系列的样本数据，以便之后输入模型进行训练或预测。\n",
    "#具体来说，假设原始的时间序列数据为 $[x_1, x_2, ..., x_n]$，那么代码首先定义了两个空列表 `X` 和 `Y`，然后在一个 for 循环中对原始数据进行了以下处理：\n",
    "#1. 对于每个下标 `i`，从原始数据中切分出长度为 `sequence_len` 的子序列 `all_data[i:i+sequence_len]`，并将该子序列添加到 `X` 列表中；\n",
    "#2. 将切分后的子序列的下一个元素 `all_data[i+sequence_len]` 添加到 `Y` 列表中。\n",
    "#最终，`X` 和 `Y` 数组将分别包含所有通过滑窗切分得到的样本数据和对应的标签数据。其中，`X` 的形状为 `(n-sequence_len, sequence_len)`，\n",
    "#表示共有 `n-sequence_len` 个样本，每个样本有 `sequence_len` 个特征值；而 `Y` 的形状为 `(n-sequence_len,)`，表示每个样本对应一个标签（即下一个时间步的特征值）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(activation_name,):\n",
    "    model = Sequential()\n",
    "    #model.add(LSTM(128, input_shape=(sequence_len,1),return_sequences=True))  #原来的\n",
    "    #model.add(LSTM(128, input_shape=(sequence_len,1),return_sequences=True))\n",
    "    #model.add(LSTM(64))\n",
    "   # model.add(Dense(1,activation=activation_name))\n",
    "    #optimizer =tf.optimizers.Adam(learning_rate=0.1) #原来的\n",
    "    optimizer =tf.optimizers.Adam(learning_rate=0.05)\n",
    "    #model.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['mape'])\n",
    "    model.add(keras.layers.LSTM(units=32, input_shape=(None, 1)))\n",
    "    model.add(keras.layers.Dense(units=1))\n",
    "    model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "    return model\n",
    "\n",
    "#原解析：\n",
    "#这些代码是用来构建一个神经网络模型，主要应用于时间序列数据的预测。具体作用如下：\n",
    "#导入Keras库中的Sequential顺序模型和LSTM层\n",
    "#定义了一个函数build_model，用于构建神经网络模型\n",
    "#在模型中添加了两个LSTM层，分别为含有128个节点的输入层和含有64个节点的输出层\n",
    "#向模型添加了一个具有1个节点的稠密层(Dense)，并指定激活函数为用户提供的activation_name参数（实现可配置）\n",
    "#定义了优化器Adam，并设置学习率为0.1\n",
    "#编译了模型，指定损失函数为均方差误差（mean_squared_error），优化器为Adam，并使用平均绝对百分比误差（mean absolute percentage error）进行模型评估\n",
    "#返回构建好的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm = build_model(\"sigmoid\")\n",
    "step = 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1115, 60, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = lstm.fit(\n",
    "    X_train, Y_train, epochs=50, batch_size = 32,verbose=0,validation_data = (X_test, Y_test)\n",
    "    )\n",
    "\n",
    "#epochs (轮数): 整个训练集数据将被用来训练神经网络50次。\n",
    "#batch_size (批处理大小)：每个训练批次包含的样本数量为32。\n",
    "#verbose (冗长模式): 设置为0以关闭详细输出。\n",
    "#validation_data (验证数据): 将测试集作为验证数据传入，以了解模型的表现情况。\n",
    "#总体来说，lstm.fit()方法会根据提供的训练数据和超参数对模型进行训练，\n",
    "#并且返回一个 history 对象，其中包含一些有关模型表现的统计信息（例如每轮结束后的训练和验证损失、准确度等）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 6ms/step\n"
     ]
    }
   ],
   "source": [
    "Y_predict = lstm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_predict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59, 60, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_predict_real = stand_scaler.inverse_transform(Y_predict.reshape(-1,1))\n",
    "Y_test_real = stand_scaler.inverse_transform(Y_test.reshape(-1,1))\n",
    "#stand_scaler.inverse_transform()函数是用来将经过标准化的数据还原回其原始状态。\n",
    "#这里，Y_predict_real 和 Y_test_real就是通过逆向变换重新获得实际的数值的数组。\n",
    "#reshape()函数则是用来改变数据形状的，由于标准化后的数组是一个列向量，所以需要使用reshape(-1,1)将其转化为行向量。\n",
    "#reshape（行，列）可以根据指定的数值将数据转换为特定的行数和列数\n",
    "#这里的-1被理解为unspecified value，意思是未指定为给定的。如果我只需要特定的行数，列数多少我无所谓，我只需要指定行数，那么列数直接用-1代替就行了，\n",
    "#计算机帮我们算赢有多少列，反之亦然。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19e0c6eb6a0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 2))\n",
    "plt.plot(Y_predict_real/(1024*1024))\n",
    "plt.plot(Y_test_real/(1024*1024))\n",
    "#其中figsize参数用于设置图形窗口的大小。\n",
    "#对于Y_predict_real和Y_test_real进行归一化处理，将返回的结果转换为单位为MB（兆字节），即除以1024*1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "根均方误差(RMSE)：0.09023123862370759\n",
      "平均绝对百分比误差(MAPE)：0.3395939304942712\n"
     ]
    }
   ],
   "source": [
    "def MAPE(true, pred):\n",
    "    diff = np.abs(np.array(true) - np.array(pred))\n",
    "    return np.mean(diff / true)\n",
    "def RMSE(predictions, targets):\n",
    "    return np.sqrt(((predictions - targets) ** 2).mean())\n",
    "\n",
    "print(f\"根均方误差(RMSE)：{RMSE(Y_predict_real/(1024*1024), Y_test_real/(1024*1024))}\")\n",
    "print(f\"平均绝对百分比误差(MAPE)：{MAPE(Y_predict, Y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "请输入需要预测的数量200\n"
     ]
    }
   ],
   "source": [
    "N=int(input(\"请输入需要预测的数量\"))\n",
    "#不能输入太多 200左右 预测效果还行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all_data[-sequence_len:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
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     ]
    }
   ],
   "source": [
    "current_seq = all_data[-sequence_len:]  # 获取最后一次的输入序列\n",
    "prediction = []                          # 存放模型预测结果\n",
    "for i in range(N):\n",
    "    # 对当前序列进行预测并加入到结果数组中 \n",
    "    predicted_value = lstm.predict(current_seq.reshape(1, sequence_len, 1))\n",
    "    prediction.append(predicted_value[0,0])\n",
    "    # 将当前结果作为下一次的输入序列的一部分\n",
    "    a=current_seq[1:]\n",
    "    b=predicted_value\n",
    "    current_seq = np.concatenate([a,b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "未来200个时刻的预测值为：\n",
      " [[ 91124.445]\n",
      " [122071.42 ]\n",
      " [143775.52 ]\n",
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      " [ 29675.875]\n",
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      " [338602.8  ]\n",
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      " [489820.4  ]\n",
      " [498076.97 ]\n",
      " [342024.5  ]\n",
      " [113006.16 ]\n",
      " [ 30440.531]\n",
      " [ 86002.1  ]\n",
      " [143097.12 ]\n",
      " [174410.16 ]\n",
      " [203035.55 ]\n",
      " [240161.48 ]\n",
      " [277346.28 ]\n",
      " [307267.72 ]\n",
      " [328228.9  ]\n",
      " [340307.44 ]\n",
      " [344086.53 ]\n",
      " [341820.16 ]\n",
      " [338188.1  ]]\n"
     ]
    }
   ],
   "source": [
    "prediction_real = stand_scaler.inverse_transform(np.array(prediction).reshape(-1,1))\n",
    "print(\"未来\"+str(N)+\"个时刻的预测值为：\\n\", prediction_real)  # 打印未来N个时刻的预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#改数据格式\n",
    "prediction_real_change=[]\n",
    "for j in prediction_real:\n",
    "    prediction_real_change.extend(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存数据\n",
    "# data3=pd.DataFrame()\n",
    "# data3['预测']=prediction_real_change\n",
    "# data3.to_excel('预测结果.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19e11f57970>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,5 ))\n",
    "plt.plot(prediction_real_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
